System and method for part identification using 3D imaging

ABSTRACT

The system and method deal with three-dimensional (3D) scanning technology which produces object representations which permit rapid, highly-accurate part identification which is not afforded by traditional two-dimensional (2D) camera imaging. The system and method are applicable to any field wherein repair/replacement parts are needed, such as the plumbing, automotive, fastener, marine, window, door, etc. fields.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority from U.S. Provisional PatentApplication No. 62/354,603 filed on Jun. 24, 2016, of the same title,the content of which is incorporated herein in its entirety, byreference.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention deals with three-dimensional (3D) scanningtechnology which produces object representations which permit rapid,highly-accurate part identification which is not afforded by traditionaltwo-dimensional (2D) camera imaging.

Prior Art

Parts replacement is a huge business, and an automated approach toidentification is long overdue. Almost every industry today shares thecommon problem of maintaining repair parts for rapidly expanding productlines. Products are constantly changing; for instance, becoming moreenergy efficient, more current in fashion trends, or moretechnologically advanced. Over time, the ability to identify and repairspecific product lines has become increasingly difficult. Each new lineadds a multitude of replacement parts to a seemingly endless list thatcontinues to accumulate for decades. The sheer volume of information isoften overwhelming for all but the most expert individuals in aparticular field. Only such experts possess the knowledge and experiencenecessary to identify these parts, and obtaining that knowledge requiresyears or decades of exposure to the lines. Parts identification has thuslargely become a lost art, which has led large retailers to only attemptidentification of the most common and readily available items.

A prime example of these trends is found in the plumbing parts industry.An active plumbing parts market began in the 1920s as residentialplumbing became commonplace. Originally plumbing manufacturing was alocalized industry, but over time some brands grew to be regional andnational, dramatically increasing the number of fixtures in the fieldand, in turn, the difficulty of parts identification. As product lineshave evolved, many manufacturers have failed to keep track of their ownparts for discontinued lines. Furthermore, in the past two decades,imported products sold through home centers have flooded the market;these fixtures have virtually no manufacturer support in terms of partsidentification nor inventory. Finally, because the plumbing industry hasbecome a fashion industry, entire product lines can change at the whimof fashion trends, compounding the difficulty of parts identificationfurther. Some manufacturers still maintain some of their obsolete parts,and non-OEM manufacturers have replaced many thousands of discontinueditems. As a result, today there are decades of parts available, butaverage consumers and distributors have no way of identifying theseparts quickly and accurately. In the present exemplary method, a massivecross-referenced database of thousands of plumbing repair parts has beencreated, but even this database is so massive that only an expert cansift through it efficiently.

A number of recognition methods have been developed that rely solely onimages both for off-line database models and for online recognition witha few significant differences from that proposed herein. Firstly, thepresently available systems capture only a two-dimensional (2D)projection of an object, with no depth information. The third dimensionmust be inferred indirectly, for instance by using a very large numberof photographs at different angles to train each object type, or is notused at all, severely limiting performance. In addition to lackingdepth, 2D projections also carry no notion of absolute scale, meaningthat it is impossible to determine the true physical dimensions of anobject solely from images in meaningful real-world units such as inchesor millimeters. There is an inherent ambiguity that couples the actualobject size with its distance from the camera; i.e., a part that istwice as large imaged from twice as far away will look the same in aphotograph, and thus cannot be distinguished.

Secondly, the appearance of objects in photographs is subject to manyexternal and uncontrolled factors that can dramatically altermeasurements from one image to another, and thus makes recognition evenmore challenging. Environmental lighting causes reflections and glare,casts shadows, and changes the apparent color and lightness of objectsurfaces. Radiometric characteristics of the imager itself, includingcolor response curves of the photodetector, white balance and gainadjustments, aperture and shutter settings, focus, etc., also affectobject appearance, sharpness, and color. The perspective and distancefrom which an object is viewed, along with geometric lens properties ofa camera used in taking the photographs, can distort the object'svisible shape, vary its appearance, and cause self-occlusions thatdiffer from image to image.

Two 2D parts recognition solutions currently available are provided by:

PartPic

75 5th St. NW Suite 2240

Atlanta, Ga. 30308

partpic.com/and

FindBox

BundesstraBe 16

77955 Ettenheim

findbox.de/en

The exemplary 3D part recognition system of the present invention allowsfor quicker, and far more accurate product identification than affordedby 2D scanning, with minimal user training.

With respect to data acquisition, non-contact 3D scanners fall into twobroad categories: passive sensing, such as binocular stereoscopic visionor monocular structure-from-motion, which uses only cameras; and activesensing, projecting light onto scanned objects and measuring theresponse with a camera to infer depth. Active scanners can be furtherpartitioned into two subcategories:

1. Time of flight. Pulses of light are generated by an emitter, and aphotodetector or camera measures the phase shift and thus the time takenfor the pulse to travel to the object and return. This time is directlyproportional to the distance from the emitter to the scanned surface,allowing range values to be easily computed. Line scan ranging devicesuse a single laser beam combined with a spinning mirror, and thus onlymeasure a single bearing range pair at each sample, while area-pulseddevices use a broader wave front and thus can measure range over anentire photodetector array at once.

2. Structured light. A projector, typically a laser diode or digitallight processing (DLP) device, emits a known pattern of light, such as astripe, dot array, or fringe pattern, that illuminates the scannedobject's surface. A precisely calibrated camera records the reflectionof this pattern, and the degree of measured geometric deformation of thepattern allows calculation of the surface's shape.

Raw output formats of individual scans include bearing range sequences,depth images, and point clouds. Most scanning systems also allow placingthe scanned object on a turntable, or physically moving the sensoraround the object, to collect multiple scans so that all surfaces arevisible. Software can then automatically “stitch” the different viewstogether into a single coherent model, which can be exported to avariety of standard formats, including regularly sampled 3D point cloudsor watertight meshes consisting of vertices and triangles.Red-green-blue (RGB) color values from the scanner's imager can also beassigned to each point or mesh vertex to form texture maps that encodethe visible appearance of the object's surfaces. Under controlledconditions, high quality scanners produce very detailed 360 degreeobject models with sub-millimeter resolution, precisely capturing evenvery fine scale features such as screw threading and surface embossing.Furthermore, models can be acquired very quickly, especially by areapattern structured light scanners that image large portions of theobject's surface simultaneously. These qualities make 3D scanning viablefor large scale parts database acquisition and for discriminatingbetween multiple parts with subtle shape differences.

With respect to part identification via 3D scans, a number of methodshave been developed in the scientific literature for recognizing objectsbased on their 3D shape. A prototypical object recognition systemoperates in two phases:

A.1. Training. This is an offline process and is only required when newobject models (or part SKUs) are added. The recognition system is fed aseries of well-curated, labeled scans of the objects to be recognized,and accesses or “learns” a database that associates each object labelwith a summary representation or model of that object, such as spatialdimensions, 3D point clouds, or set of local geometric descriptors.

A.2. Query. This is an online process that is invoked whenever anunknown object or part is to be recognized. The system converts a 3Dscan of the “query” object into a summary representation commensuratewith that stored in the training database. The method then proceeds todetermine a score, or degree of similarity, between the query object andeach of the learned models in the database. The system then reports anordered list of the top matching labels and their associated scores.

B. Two key factors in recognition system performance are objectrepresentation (i.e features) and matching criteria (i.e. method bywhich features are compared). There are several broad choices forrecognition strategies, including:

B.1. Hierarchical. A cascaded series of simple, rapidly-computedattributes such as size and shape that partition the database into atree-like structure. Within each level of the tree is a finer degree ofspecificity. This permits very efficient query even with a very largenumber of models.

B.2. Holistic. The entire object, for instance in the form of a pointcloud, is geometrically matched against each model in the database in anexhaustive fashion. This method works best for rigid, non-articulatedobject types, and while highly accurate, can be somewhat slow anddependent on precise object-to-object alignment.

B.3. Feature-based. A set of local geometric descriptors is computed foreach object model and for the query object in the form ofmultidimensional feature vectors with associated 3D spatial locations.The feature vectors extracted from the query object are compared tothose extracted from the database models, with geometric consistencycriteria applied across the set of feature matches. This method is veryfast and exhibits robustness to missing information, clutter, andmisalignment.

Aspects of these strategies may also be combined in various ways toachieve desired levels of accuracy and speed. Matching methods work bestwhen (1) objects to be recognized have predictable shape, (2) scans areof high quality and detail, (3) objects are scanned in isolation fromother objects and background clutter, and (4) objects are inapproximately known orientation when scanned. Under benign andcontrolled conditions, recognition rates can exceed 99% accuracy withvery few false positives, even for large model databases. The degree ofgeometric similarity between models in the database is also an importantfactor in how reliably objects can be distinguished from one another,and the number of different models determines the speed with whichresults can be generated.

It will be understood that many industries, such as plumbing,automotive, fastener, marine, window and door, etc., have continuallygrowing product lines, and as new models flood the market, the inventoryof repair and replacement parts continues to grow as well. Personnelwith the expertise required to identify parts across an entirediscipline are increasingly rare, since acquiring this knowledgerequires decades of experience. As a result, large retail stores likeLowes, Home Depot, Ace Hardware, etc. teach their personnel “PriorityParts Identification” only; these are items that are most frequentlyrequested at the store.

SUMMARY OF THE INVENTION

The object recognition approach of the present invention relating topart identification involves four main components.

A. DATABASE: A comprehensive catalog consisting of 3D object scans iscreated a-priori for training and analyzed by the system to producedigital feature models. These models form the reference database used bythe system to recognize query objects.

B. USER-BASED SCANNING: End users scan unknown objects or parts with anyelectronic instrument—such as their own mobile device capable of 3Dimaging or a dedicated 3D scanner installed at a retail location—to formqueries. A query scan is then analyzed by the system to produce adigital profile compatible with the database.

C. MATCHING SYSTEM: A computerized computation system compares featuresin the query profile with features in the database via hierarchical,holistic, and/or feature-based methods. Candidate matches are rankedaccording to a score value.

D. DATA PRESENTATION: Ranked match results, along with part numbers,images, and other descriptive information, can be displayed to the user(e.g., for online order fulfillment). Results can also be routed tovarious other back end services for further processing, analytics, orvisual search.

In the Plumbing section, for instance, Priority Parts are typicallystems and cartridges. Most retail stores maintain inventory and are ableto readily identify approximately 100 stems and cartridges at any giventime, but more than 2,000 different types actually exist. It has provennearly impossible for stores and wholesalers to maintain personnel withthe requisite expertise to identify these additional 1,900 items, somost locations do not carry inventory other than the Priority Parts. Asa result, the consumer is often left unsatisfied, or even forced to buyan entirely new fixture, which is both expensive and wasteful.

The present recognition system will solve these problems by allowingstore employees and consumers to quickly and automatically identifyunknown parts with no prior product-specific experience. The system willaccommodate a variety of user-based scanning methods, including 3Dscanners placed in retail stores, consumer-grade depth cameras emergingon mobile phones, etc. The part databases will be comprehensive,containing both common and rare items, so that identification andfulfillment do not depend on particular inventory or experience thathappens to be on hand. Users will simply place a part of interest infront of the scanner, and the system will efficiently provide a rankedlist of most likely catalog part matches.

With respect to advantages of the system and method of the presentinvention, 3D scanners overcome nearly all of the shortcomings ofexisting image-based solutions by measuring an object's surfacesdirectly. They recover object shape and true size in all threedimensions simultaneously, with no ambiguities arising from distance,viewpoint, lighting, or occlusion. Because 3D scanners measure true,objective properties (physical distance) rather than apparent,subjective properties (projection and reflectance), they produceinherently more reliable observations that can be meaningfully comparedfor much more accurate object recognition. Furthermore, building largedatabases is a more straightforward process, requiring fewerobservations of each object type, and models can even be obtainedthrough other means such as CAD drawings.

The use of 3D scanners thus affords the system significant advantages interms of accuracy and efficiency, which allow for the building of theparts databases much more quickly, and allow parts to be recognized muchmore easily and effectively, than existing solutions.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 presents a system level logic flow diagram used by thecomputerized system and method of the present invention.

FIG. 2 presents a logic flow diagram for creation of a database ofparticular parts.

FIG. 3 presents a logic flow diagram of database creation in connectionwith a part matching process of the present invention.

FIG. 4 shows a customer bringing in a part for identification by thesystem and method of the present invention, in this example a plumbingcartridge.

FIG. 5 shows the cartridge of FIG. 4 being placed on a turntable forscanning and identification.

FIG. 6 shows the cartridge being scanned by a countertop 3D scanner, asan example, creating a 3D point cloud model.

FIG. 7 shows the 3D point cloud model created by the scan of the partand the model is compared to all the cartridge models and theirparameters stored in the system database.

FIG. 8 shows a screenshot of potential matching parts retrieved from thesystem, in order of match percentage.

FIG. 9 shows a screenshot of the highest ranking part selected and showsa button for visualization in all dimensions, if desired.

FIG. 10 shows what appears when the button of FIG. 9 is activated.

FIG. 11 shows activation of a measuring tool of the system which bringsup point to point measurements for comparison with the actual part.

FIG. 12 shows a screenshot of an order page for the correct part, whichalso provides a list of related parts which may also potentially beneeded.

DESCRIPTION OF THE PREFERRED EMBODIMENT

Turning to FIG. 1, there is illustrated therein a low level system levellogic flow diagram 10 wherein a user has a physical object (part) thathe/she wishes to identify, e.g., for the purpose of replacement orre-ordering. Some parts may be easily identified by visual inspection 12from information imprinted on the surface, prior experience, distinctiveshape, size, etc. If such is present, and the part is visuallyidentifiable 14, then one merely verifies that the part is correct 16,and takes action, ordering the proper part at 18. However, many are notreadily identifiable, perhaps due to uncommon brand, outdated product,damaged/dirty condition, or unfamiliarity. In such a case, the usercaptures the part parameters with a 3D scanning device at 20. Theresulting scan data is transmitted at 22 to a computerized recognitionsystem that resides either locally or on a remote network.

The data is then processed to extract a profile consisting of geometricand photometric descriptors 24 suitable for part identification. Thecomputerized recognition system compares this profile against acorresponding database 26 of previously acquired profiles, eliminatesvery unlikely matches 28, and ranks surviving candidates according tomatch likelihood. The associated identifiers (e.g., part numbers) forthe best matches, along with their likelihoods, are returned andpresented to the user via graphical display 30.

If the system logic 10 returns a single match at 32, the part isverified at step 16 and ordered at step 18. If the logic 10 returns morethan one match at 32, then the user can interactively filter the resultsat 34 via the display based on manually-entered attributes and based onvisual comparison of the physical part with images, 3d models, andtextual descriptions of the candidates. Finally, when the candidate listhas been narrowed to a single matching part at 32, the user verifies thecorrect part at 16 and can act on the resulting identifier, for example,order a replacement directly from the catalog website at 18.

Turning now to FIG. 2, the corresponding database 40 of the computerizedsystem 10 used to recognize the particular part is created offline, andcontains a set of part identifiers, such as, for example, for a plumbingvalve and may be captured by any suitable means such as those at steps42, 44 or 46. Associated with each identifier is created a geometric andphotometric profile 48 compatible with that extracted from the scannedpart, as well as other information such as photographs, textualdescriptions, and human-readable attributes that can be used for manualfiltering by the user. The database may be populated by scanningphysical objects, or by other means such as acquiring CAD models frommanufacturers. Profiles are then stored at 50 to the database 40 and arederived from the initial 3d representation in the same manner as fromthe user-scanned part. Definitions of terms used herein are listed belowfor ease of comprehension:

part: a specific physical object with [mostly] rigid 3d shape; forinstance, a faucet stem or mounting bracket.

user: the operator of the system; for instance, a retail store employeewishing to identify a part, or a consumer wishing to find a replacement.

scanner: a device that can create a digital 3d model of a presentedobject by measuring distance and appearance, e.g., using cameras,projectors, and/or lasers.

features: attributes of an object such as local and global 3d shape,physical dimensions, and visual appearance represented as a series ofnumeric values.

profile: a digital representation of an object that encodes one or morefeatures.

identifier: a unique name or tag uniquely identifying a particular part,such as a product number or SKU.

database: a digital catalog or repository of parts, along with theiridentifiers and profiles, that can be queried according to variousattributes.

match: a correspondence between the user-scanned part and a candidatefrom the database.

likelihood: a score or probability that a particular match is correct,or a degree of similarity between the query and the match.

recognition: the search for and retrieval of the most likely matches fora particular part from a database, as well as the likelihood of eachmatch.

query: a particular part instance that the user wishes the system torecognize.

The system distinguishes itself from competition and prior art in anumber of ways, some of which are denoted at present in FIGS. 1 and 2.Foremost among these is the use of 3D information at all stages,including database creation, part query, part matching, and userinteraction. While other methods may share similar overall logicstructure, they rely solely on digital images instead, which areinherently 2D projections of an object's true 3D shape.

Particular items of note:

-   -   (1) A digital 3D representation of the query part is acquired        (e.g., via turntable scanner or mobile device) and used for        matching by the system against the database information stored        in memory. This representation captures the part's true size and        shape in real-world units. Other methods acquire 2d images only.    -   (2) The recognition system operates using 3D data, producing        geometric shape-based (and optionally appearance-based) features        that directly encode local surface structure independent of        environment and imaging conditions. Other methods exclusively        use appearance-based features, which are derived from 2d image        projections and thus (a) have no notion of true scale; (b) have        no notion of “depth” or 3d surface structure; and (c) are        affected dramatically by illumination, shadows, camera settings,        viewpoint, and environment.    -   (3) Results are presented to the user in a multitude of formats        that consist of traditional images and text annotations (these        are the only formats returned by other methods), but        additionally present rotatably viewable 3d models stored in the        database.    -   (4) Retrieving 3D models, and encoding their true dimensions,        allow the user to further narrow the search results via        interactive inspection (e.g., manipulating the candidate models        in a 3D viewer) and via manual data entry (e.g., specifying part        length and diameter). Other methods allow only for visual        inspection of static images and textual descriptions.    -   (5) As with queries, models acquired for database generation are        also inherently 3D. This allows alternate “virtual” or “ideal”        sources such as CAD files to populate the database without        needing to obtain physical parts. Furthermore, only a single        (partial or complete) scan suffices to represent each part.        Other methods require imaging physical parts, and often involve        dozens or hundreds of examples for training.    -   (6) Also as with queries, the system extracts geometric        shape-based (and optionally appearance-based) features when        populating the database. These features are highly commensurate        with those extracted for queries because they rely on exactly        the same underlying geometry. With image-based methods, matching        query to database information is substantially more difficult        and requires much more extensive imaging of each part in order        to sufficiently capture expected variations in size, viewpoint,        and illumination.

Turning now to FIG. 3, the figure depicts the computerized process 60for (a) database creation and its use in (b) matching in more detail. Inboth pipelines, the input data is pre-processed at 62 to properly scaleand orient the part, and to create a low-level representation (e.g., 3dpoint cloud and differential orientation at each point); also in bothpipelines, these low-level representations are used to extract bothglobal features at 64 and local features at 66. Global or holisticfeatures describe the entire part and provide compact, coarsecharacteristics such as overall shape and dimensions, while localfeatures encode finer shape detail localized to small regions on thepart's surface, with both being stored in database 40. All features areindexed via efficient hashing and either stored to the database 40 orused to query the database 40.

The recognition process begins by considering all possible databasemodels for a particular query at 70 and then applies a sequence ofprogressively more complex (and more selective) filters. The first ofthese filters, global feature extraction 72, consider only holisticquery descriptors, providing a coarse classification at 74 that can becompared very quickly to those in the database 40 so as to immediatelyeliminate most possible candidates, resulting in set A. Next, localdescriptors 76 extracted from the query part are hashed at 78 and usedto efficiently scan the database's feature index, further reducingpossible matches to set B. Finally, the system evaluates each survivingcandidate in greater detail at 80 to determine its completesurface-to-surface similarity to the query; this process produces thefinal list of candidates, along with similarity scores that can be usedfor ranking and user presentation.

FIG. 4 shows a customer 100 bringing in a part 102 for identification bythe system and method of the present invention, in this example aplumbing cartridge 102. Although the example throughout deals with aplumbing part this should not be construed as limiting inasmuch as themethod and system may be used in any other field whererepair/replacement parts are required, such as for example in theautomotive part industry, etc.

FIG. 5 shows the part 102, in the form of the cartridge 102 of FIG. 4,being placed on a turntable 104 for 3D scanning and identification bythe system and method of the present invention.

FIG. 6 shows the cartridge 102 being scanned by a countertop 3D scanner106, as an example, creating a 3D point cloud model of same. Although acountertop scanner is exemplified this should not be construed aslimiting inasmuch as any electronic device capable of capturing a 3Dimage could be used, even a future cellular phone with 3D imagingcapability.

FIG. 7 shows the 3D point cloud model 108 created by the scan of thepart and the model and its parameters are compared to all the cartridgemodels and their parameters stored in the particular plumbing cartridgedatabase 40 created for the system and stored at 50 in the memorythereof. Again, this should not be construed as limiting to theversatility of the system and method.

FIG. 8 shows a screenshot 110 of potential matching parts retrieved froma search through the database 40 in the system memory, preferably, inthe preferred embodiment, ranking down from the highest matchpercentage.

FIG. 9 shows a screenshot 112 of the highest ranking part 114 selectedand shows a link button 116 for use in presenting rotatablevisualization in all dimensions, if desired.

FIG. 10 shows what appears in the next screenshot 118 when the linkbutton 116 of FIG. 9 is activated to provide rotatability to the view ofthe part 102.

FIG. 11 shows a screenshot 120 including point to point measurements 122which may be elicited from the system and method upon activation of ameasuring tool 124 of the system which brings up a point to pointmeasurement table 122 of measurements along each axis for comparisonwith the dimensions of the actual part 102.

FIG. 12 shows a screenshot 128 of an order page for the selected,highest ranking part 102, which order page may also provide a list ofrelated parts 130 which may also potentially be needed.

As described above, the system and method of the present inventionprovide a number of advantages, some of which have been described aboveand others of which are inherent in the invention.

Also, modifications may be proposed without departing from the teachingsherein. For example, although the description deals with repair and/orreplacement parts, the system and method may be used in identificationof new parts as well. Accordingly, the scope of the invention is only tobe limited as necessitated by the accompanying claims.

The invention claimed is:
 1. A system for use in identifying aparticular part not readily identifiable, by using 3D imaging, thesystem comprising: a computerized recognition system comprising acomputer; a non-contact 3D scanning device, wherein the non-contact 3Dscanning device is a structured light scanner, time-of-flight 3Dscanner, depth camera, or mobile device comprising a depth camera andproduces 3D point clouds or geometric 3D surfaces; means for thenon-contact 3D scanning device to make a 3D model of the particular partnot readily identifiable which provides a 3D imaging scan, along withgeometric descriptors and parameters, of the particular part not readilyidentifiable to be identified from information in a database within thecomputerized recognition system; the database within the computerizedrecognition system comprising profiles comprising 3D imaging scans,associated identifying information, geometric descriptors, andparameters for models of readily identifiable parts; means for thecomputer accessing the database within the computerized recognitionsystem, the computer being programmed to assess the profiles against the3D image and parameters from the non-contact 3D imaging device of theparticular part not readily identifiable and compile a number ofcandidate database part matches that match the particular part notreadily identifiable; means for the computer to filter the profiles ofthe 3D imaging scans of models of the readily identifiable parts, thecomputer being programmed to compare the 3D imaging scan of theparticular part not readily identifiable to the database and filterprofiles in the database, the filter comprising a first global featureextraction, considering only holistic query descriptors and providing acoarse classification that is compared to the profiles in the databaseto reduce the number of candidate database part matches, a second filterwherein local descriptors extracted from a query are hashed and used toscan a feature index, further reducing possible matches and a thirdfilter wherein surface-to-surface similarity is compared to that of thequery producing a final list of candidate database part matches, alongwith similarity scores used for ranking and presentation; means for thenon-contact 3D scanning device to communicate with the computer and itsdatabase, the computer providing the profile of the readily identifyingpart to a user of the non-contact 3D scanning device for at least onemodel of the particular part not readily identifiable that fits theparameters of the particular part not readily identifiable scanned bythe non-contact 3D scanning device; and the computer being programmed tointeractively filter the profiles available in the associated databaseby inputting parameters for physical attributes of the particular partnot readily identifiable, wherein the physical attributes comprisetextual descriptions or geometric descriptors.
 2. The system of claim 1wherein the database includes not only 3D scans or images but alsoassociated feature representations, including but not limited to: partmeasurements, dimensions, or local or global geometric surface shapedescriptors, which are stored as digital profiles.
 3. The system ofclaim 1 wherein the database can be populated by scanning physicalparts, or by obtaining and inputting 3D computer-aided design drawings.4. A method for identifying a particular part not readily identifiable,by using 3D imaging, using a computerized system comprising: anon-contact 3D scanning device comprising a means for producing a 3Dimaging scan of the particular part not readily identifiable to beidentified from information in a database, wherein the non-contact 3Dimaging device is a structured light, time-of-flight 3D scanner, depthcamera or mobile device comprising a depth camera that produces 3D pointclouds or geometric 3D surfaces; a computer; the database comprisingprofiles for models of readily identifiable parts, profiles comprising3D imaging scans, identifying information, geometric descriptors, andparameters for readily identifiable parts; the computer comprising meansfor accessing a computerized recognition system that stores and allowsaccess to the database and being programmed to assess profiles stored inthe database against a 3D image and parameters from the non-contact 3Dscanning device via a series of one or more holistic, hierarchical,and/or feature-based filters that operate on extracted profiles, filterscomprising a first global feature extraction, considering only holisticquery descriptors and providing a coarse classification that is comparedto the profiles in the database to reduce a number of candidate databasepart matches, a second filter wherein local descriptors extracted from aquery are hashed and used to scan a feature index, further reducingcandidate database part matches and a third filter whereinsurface-to-surface similarity is compared to that of the query producinga final list of candidate database part matches, along with similarityscores used for ranking and presentation; a communication networkbetween the non-contact 3D scanning device and the computer, thecomputer being programmed to compare the 3D image received from thenon-contact 3D scanning device to the profiles in the database andprovides information to a user of the non-contact 3D scanning device forat least one candidate database part match which fits the parameters ofthe particular part not readily identifiable scanned by the non-contact3D imaging device, the method comprising the steps of: creating the 3Dimage of the particular part not readily identifiable using thenon-contact 3D scanning device; using the computer to assess or comparethe 3D image of the particular part not readily identifiable providedfrom the non-contact 3D scanning device to the database; using thecommunication network to send the 3D image of the particular part notreadily identifiable to the computer for comparison to the profiles inthe database; providing at least one candidate database part match whichfits the parameters of the particular part not readily identifiablescanned by the non-contact 3D scanning device; and using the filters tointeractively filter the profiles in the database by inputtingparameters for physical attributes of the particular part not readilyidentifiable, wherein the physical attributes comprise textualdescriptions or geometric descriptors.
 5. The method of claim 4 whereinthe non-contact 3D scanning device comprises sensors or scanners used toscan parts that need to be identified, producing measurements inreal-world scale units, and extracting digital profiles commensuratewith profiles stored in the database.
 6. The method of claim 5 whereinscanned part information is transmitted to the computer and compared asa query profile to information stored in the database to identify mostlikely candidate database part matches.
 7. The method of claim 5,wherein the candidate database part matches are ranked based onsimilarity to the query.
 8. The method of claim 7 wherein resultingranked candidate database part matches are provided to the user, alongwith images, text descriptions, pricing, and other associated databaseinformation.
 9. The method of claim 5 wherein the computer can alsodispatch results to a 3rd party inventory management website or softwareservice.